Results for “molecular-descriptors”
15 skills
k-dense-ai Datamol
Simplify molecular cheminformatics with a Pythonic wrapper around RDKit for SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing.
30.2k · bundle
lingxling Rdkit
Provides guidance for using RDKit to read and write molecular structures, calculate descriptors, generate fingerprints, perform substructure searches, and handle chemical reactions.
253 · bundle
k-dense-ai Rdkit
Perform cheminformatics tasks including molecular I/O, descriptor calculation, fingerprinting, substructure search, and similarity analysis using the RDKit library.
30.2k · bundle
More results
lingxling Datamol
Pythonic wrapper around RDKit for cheminformatics, simplifying SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing while returning native rdkit.Chem.Mol objects.
253 · bundle
k-dense-ai Deepchem
Predict molecular properties, train graph neural networks, and run drug discovery workflows using DeepChem's featurizers, models, and MoleculeNet benchmarks.
30.2k · bundle
k-dense-ai Pytdc
Access AI-ready drug discovery datasets and benchmarks from Therapeutics Data Commons, covering ADME, toxicity, drug-target interactions, and molecular generation with standardized splits and evaluation metrics.
30.2k · bundle
lingxling Pytdc
Access AI-ready drug discovery datasets, benchmarks, and molecular oracles from Therapeutics Data Commons for therapeutic machine learning and pharmacological prediction.
253 · bundle
k-dense-ai Medchem
Apply medicinal chemistry filters for compound triage: drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and a custom query language for library filtering.
30.2k · bundle
lingxling Medchem
Filters and prioritizes compound libraries in drug discovery using drug-likeness rules, structural alerts, complexity metrics, and a query language.
253 · bundle
k-dense-ai Molfeat
Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning using 100+ featurizers, including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa.
30.2k · bundle
lingxling Matchms
Process and analyze mass spectrometry data with the Matchms Python library, including importing spectra, filtering peaks, calculating similarity scores, and building reproducible analytical workflows.
253 · bundle
k-dense-ai Torchdrug
Build and train graph neural networks for drug discovery, protein modeling, and molecular science using PyTorch-native tools.
30.2k · bundle
k-dense-ai Molecular Dynamics
Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces).
30.2k · bundle
lingxling Molfeat
Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning, covering 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa, with support for QSAR modeling and virtual screening.
253 · bundle
alterlab-ieu Alterlab Rdkit
Provides the RDKit cheminformatics toolkit for low-level, fine-grained molecular primitives — SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure/SMARTS search, 2D/3D coordinate generation, similarity, and reaction handling. Use when custom sanitization, specialized fingerprint or descriptor algorithms, reaction enumeration, or conformer generation demand direct API control; for a high-level pandas-friendly wrapper over RDKit prefer alterlab-datamol, and for turning molecules into ML feature vectors prefer alterlab-molfeat. Part of the AlterLab Academic Skills suite.
60 · bundle